دانشگاه تربیت دبیر شهید رجائی
انتشارات دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات11
تعداد شماره‌ها210
تعداد مقالات2,098
تعداد مشاهده مقاله2,877,475
تعداد دریافت فایل اصل مقاله2,085,248
Behravan, I., Razavi, S.M.. (1401). Application of Harris Hawks Optimization Algorithm and APSO-CLUSTERING in Predicting the Stock Market. فناوری آموزش, 10(2), 447-462. doi: 10.22061/jecei.2022.8659.541
I. Behravan; S.M. Razavi. "Application of Harris Hawks Optimization Algorithm and APSO-CLUSTERING in Predicting the Stock Market". فناوری آموزش, 10, 2, 1401, 447-462. doi: 10.22061/jecei.2022.8659.541
Behravan, I., Razavi, S.M.. (1401). 'Application of Harris Hawks Optimization Algorithm and APSO-CLUSTERING in Predicting the Stock Market', فناوری آموزش, 10(2), pp. 447-462. doi: 10.22061/jecei.2022.8659.541
Behravan, I., Razavi, S.M.. Application of Harris Hawks Optimization Algorithm and APSO-CLUSTERING in Predicting the Stock Market. فناوری آموزش, 1401; 10(2): 447-462. doi: 10.22061/jecei.2022.8659.541

Application of Harris Hawks Optimization Algorithm and APSO-CLUSTERING in Predicting the Stock Market

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 17، دوره 10، شماره 2، مهر 2022، صفحه 447-462    اصل مقاله (1.01 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.8659.541
نویسندگان
I. Behravan؛ S.M. Razavi*
Department of Electrical Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 10 آذر 1400،  تاریخ بازنگری: 21 بهمن 1400،  تاریخ پذیرش: 22 فروردین 1401 
چکیده
Background and Objectives: Stock markets have a key role in the economic situation of the countries. Thus one of the major methods of flourishing the economy can be getting people to invest their money in the stock market. For this purpose, reducing the risk of investment can persuade people to trust the market and invest. Hence, Productive tools for predicting the future of the stock market have an undeniable effect on investors and traders’ profit.
Methods: In this research, a two-stage method has been introduced to predict the next week's index value of the market, and the Tehran Stock Exchange Market has been selected as a case study. In the first stage of the proposed method, a novel clustering method has been used to divide the data points of the training dataset into different groups and in the second phase for each cluster’s data, a hybrid regression method (HHO-SVR) has been trained to detect the patterns hidden in each group. For unknown samples, after determining their cluster, the corresponding trained regression model estimates the target value. In the hybrid regression method, HHO is hired to select the best feature subset and also to tune the parameters of SVR.
Results: The experimental results show the high accuracy of the proposed method in predicting the market index value of the next week. Also, the comparisons made with other metaheuristics indicate the superiority of HHO over other metaheuristics in solving such a hard and complex optimization problem. Using the historical information of the last 20 days, our method has achieved 99% accuracy in predicting the market index of the next 7 days while PSO, MVO, GSA, IPO, linear regression and fine-tuned SVR has achieved 67%, 98%, 38%, 4%, 5.6% and 98 % accuracy respectively.
Conclusion: in this research we have tried to forecast the market index of the next m (from 1 to 7) days using the historical data of the past n (from 10 to 100) days. The experiments showed that increasing the number of days (n), used to create the dataset, will not necessarily improve the performance of the method.
کلیدواژه‌ها
Tehran Stock Market؛ Harris Hawks Optimization (HHO)؛ Support Vector Regression (SVR)؛ APSO-Clustering؛ Metaheuristics
مراجع

[1] H. Chen, D.L. Fan, L. Fang, W. Huang, J. Huang, C. Cao, et al., "Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis," Int. J. Pattern Recognit. Artif. Intell., 34: 2058012, 2020.

[2] M. Janga Reddy, D. Nagesh Kumar, "Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review," H2Open J., 3: 135-188, 2021.

[3] Q.V. Pham, D.C. Nguyen, S. Mirjalili, D.T. Hoang, D.N. Nguyen, P.N. Pathirana, et al., "Swarm intelligence for next-generation wireless networks: Recent advances and applications," arXiv preprint arXiv:2007.15221, 2020.

[4] M. Schranz, G.A. Di Caro, T. Schmickl, W. Elmenreich, F. Arvin, A. Şekercioğlu, et al., "Swarm intelligence and cyber-physical systems: concepts, challenges and future trends," Swarm Evol. Comput., 60: 100762, 2021.

[5] A. Kaveh, A.D. Eslamlou, Metaheuristic optimization algorithms in civil engineering: New applications: Springer, 2020.

[6] B. Yang, J. Wang, X. Zhang, T. Yu, W. Yao, H. Shu, et al., "Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification," Energy Convers. Manage., 208: 112595, 2020.

[7] J. Nocedal, S. Wright, Numerical optimization: Springer Science & Business Media, 2006.

[8] G. Wu, "Across neighborhood search for numerical optimization," Inf. Sci., 329: 597-618, 2016.

[9] M. Usmani, S.H. Adil, K. Raza, S.S.A. Ali, "Stock market prediction using machine learning techniques," in Proc. 2016 3rd international conference on computer and information sciences (ICCOINS): 322-327, 2016.

[10] S. Pyo, J. Lee, M. Cha, H. Jang, "Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets," PloS one, 12: e0188107, 2017.

[11] M.R. Senapati, S. Das, S. Mishra, "A novel model for stock price prediction using hybrid neural network," J. Inst. Eng. (India): Ser. B, 99: 555-563, 2018

[12] H. Hu, L. Tang, S. Zhang, H. Wang, "Predicting the direction of stock markets using optimized neural networks with Google Trends," Neurocomputing, 285: 188-195, 2018.

[13] X. Pang, Y. Zhou, P. Wang, W. Lin, V. Chang, "An innovative neural network approach for stock market prediction," J. Supercomputing, 76: 2098-2118, 2020.

[14] N. Gozalpour, M. Teshnehlab, "Forecasting stock market price using deep neural networks," in Proc. 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS): 1-4: 2019.

[15] M. Ghanbari, H. Arian, "Forecasting stock market with support vector regression and butterfly optimization algorithm," arXiv preprint arXiv:1905.11462, 2019.

[16] M. Vijh, D. Chandola, V. A. Tikkiwal, A. Kumar, "Stock closing price prediction using machine learning techniques," Procedia Comput. Sci., 167: 599-606, 2020.

[17] F. Ecer, S. Ardabili, S.S. Band, A. Mosavi, "Training multilayer perceptron with genetic algorithms and particle swarm optimization for modeling stock price index prediction," Entropy, 22: 1239, 2020.

[18] M. Nabipour, P. Nayyeri, H. Jabani, S. Shahab, A. Mosavi, "Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis," IEEE Access, 8: 150199-150212, 2020.

[19] M.J. Awan, M.S. M. Rahim, H. Nobanee, A. Munawar, A. Yasin, A.M. Zain, "Social media and stock market prediction: A big data approach," Comput. Mater. Continua, 67: 2569-2583, 2021.

[20] I. K. Nti, A.F. Adekoya, B.A. Weyori, "A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction," J. Big Data, 8: 1-28, 2021.

[21] S.Tuarob, P. Wettayakorn, P. Phetchai, S. Traivijitkhun, S. Lim, T. Noraset, et al., "DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction," Financ. Innovation, 7: 1-32, 2021.

[22] M. Ali, D.M. Khan, M. Aamir, A. Ali, Z. Ahmad, "Predicting the direction movement of financial time series using artificial neural network and support vector machine," Complexity, 2021: 1-13, 2021.

[23] I. Behravan, S.H. Zahiri, S.M. Razavi, R. Trasarti, "Clustering a big mobility dataset using an automatic swarm intelligence-based clustering method," J. Electr. Comput. Eng. Innovations, 6(2): 251-271, 2018.

[24] I. Behravan, S.H. Zahiri, S. M. Razavi, R. Trasarti, "Finding roles of players in football using automatic particle swarm optimization-clustering algorithm," J. Big data, 7: 35-56, 2019.

[25] I. Behravan, S.M. Razavi, "A novel machine learning method for estimating football players’ value in the transfer market," Soft Comput., 25: 2499-2511, 2021.

[26] J.C. Bednarz, "Cooperative hunting Harris' hawks (Parabuteo unicinctus)," Science, 239: 1525-1527, 1988.

[27] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, "Harris hawks optimization: Algorithm and applications," Future Gener. Comput. Syst., 97: 849-872, 2019.

[28] S.M. Yusuf, C. Baber, "Multi-Agent searching adaptation using levy flight and inferential reasoning," Int. J. Electr. Inf. Eng., 14: 290-297, 2020.

[29] X.S. Yang, Nature-inspired metaheuristic algorithms: Luniver press, 2010.

[30] J. Cheng, D. Yu, Y. Yang, "Application of support vector regression machines to the processing of end effects of Hilbert–Huang transform," Mech.l Syst. Sig. Process., 21: 1197-1211, 2007.

[31] M. Sabzekar, S.M.H. Hasheminejad, "Robust regression using support vector regressions," Chaos, Solitons & Fractals, 144: 110738, 2021.

[32] H. Drucker, C.J. Burges, L. Kaufman, A. Smola, V. Vapnik, "Support vector regression machines," Adv. Neur. Inf. Process. Syst., 9: 155-161, 1997.

[33] I. Behravan, O. Dehghantanha, S. H. Zahiri, "An optimal SVM with feature selection using multi-objective PSO," in Proc. 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC): 76-81, 2016.

[34] B. Madhu, A.K. Paul, R. Roy, "Performance comparison of various kernels of support vector regression for predicting option price," Int. J. Discrete Math., 4: 21, 2019.

[35] Y. Tang, W. Guo, J. Gao, "Efficient model selection for support vector machine with Gaussian kernel function," in Proc. 2009 IEEE Symposium on Computational Intelligence and Data Mining: 40-45, 2009.  

[36] W. Wang, Z. Xu, W. Lu, X. Zhang, "Determination of the spread parameter in the Gaussian kernel for classification and regression," Neurocomputing, 55: 643-663, 2003.

[37] S. Mirjalili, S. M. Mirjalili, A. Hatamlou, "Multi-verse optimizer: a nature-inspired algorithm for global optimization," Neur. Comput. Appl., 27: 495-513, 2016.

[38] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, "GSA: a gravitational search algorithm," Inf. Sci., 179: 2232-2248, 2009.

[39] M.H. Mozaffari, H. Abdy, S.H. Zahiri, "IPO: an inclined planes system optimization algorithm," Comput. Inf., 35: 222-240, 2016.

[40] M.H. Mozaffari, H. Abdy, S.H. Zahiri, "IPO: an inclined planes system optimization algorithm," Comput. Inf., 35: 222-240, 2016.

[41] I. Behravan, S. Razavi, “Stock price prediction using machine learning and swarm intelligence,” J. Electr. Comput. Eng. Innovations, 8(1):31-40, 2020.

[42] R.K. Dash, T.N. Nguyen, K. Cengiz, A. Sharma, "Fine-tuned support vector regression model for stock predictions," Neur. Comp. Appl., 2021: 1-15, 2021.

آمار
تعداد مشاهده مقاله: 364
تعداد دریافت فایل اصل مقاله: 317


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب